Predicting Attacks with Deep Neural Networks

Earlier this week was the Anti-Nex release and today is the release of the first pre-trained models to defend software applications.

Here are the supported applications and model accuracies for predicting an attack:

• Flask: 89% accuracy
• React and Redux: 87% accuracy
• Vue: 83% accuracy
• Django: 70% accuracy
• Spring: 66% accuracy

The repository for building datasets and training deep neural networks using Keras and Tensorflow is on GitHub:

https://github.com/jay-johnson/antinex-datasets

Inside you can also find the first non-attack dataset for training deep neural network models to defend your own software applications, infrastructure and personal property from network exploits.

Call to Action

1. I am exploring using sentiment analysis with a Keras embedding layer and would love to hear your thoughts on if this is a good choice for training models to predict an attack within tcp payloads.
2. Help creating new attack and non-attack datasets. Let me know if you are interested.

Thanks for reading and I hope you find these models valuable.

Shields up and good luck out there!

Jay